Complex industrial equipment is often critical to production operations with significant upstream and downstream effects in the event of performance losses or equipment failure. Sensor and equipment failures, sensor calibration drift, varying process parameters and changes or disturbances in operating conditions or environment can all contribute to production problems and losses.
The fusion of sensors, equipment and dynamic processes, some of which cannot be measured cost effectively or at all, make it very difficult for production operations to identify, let alone remediate equipment and production problems. As a consequence, many companies require skilled operators or contractors to observe and identify problems and determine which actions or solutions to implement. Often sufficient information is not available on a timely basis, however decisions are still required.
Traditionally advanced control systems use defined models of processes to correct for process variations or disturbances. It can often be difficult, or not possible at all, to accurately determine, anticipate or identify the causality of variations or disturbances. Determining the best possible course of action under these circumstances is even more difficult especially when control action based on incorrect diagnosis can place the process or the product at risk.
As taught by Ken Schwaber and Mike Beedle in Agile Software Development with Scrum, (2002) Upper Saddle River: Prentice Hall, p. 25, ISBN 0-13-067634-9, retrieved Jul. 6, 2007:                “The defined process control model requires that every piece of work be completely understood. Given a well-defined set of inputs, the same outputs are generated every time. A defined process can be started and allowed to run until completion, with the same results every time.” http://en.wikipedia.org/wiki/Defined process—cite note-1        
Model predictive control techniques typically require the nature of disturbances to be known in advance in order to provide control direction to bring the process back within its normal operating envelope. Such techniques also require the parameters of the model to be accurately tuned such that inputs to the model generate accurate and relevant outputs. Such applications require significant upkeep by highly skilled and knowledgeable individuals unless they are highly stable.
Such advanced control techniques are typically addressed with an exhaustive set of logical or knowledge based rules to try and identify with certainty what the cause is and the best course of action to implement. Multiple contributions to an observable problem can create sufficient uncertainty that these systems cannot perform or there are simply too many variations to exhaustively catalog. In addition, the computational requirements to diagnose and determine appropriate corrective action can interfere with the performance of time sensitive process monitoring and control.
Addressing these issues requires a hardware and software platform capable of identifying and prioritizing real time control actions while executing advanced Artificial Intelligence (AI) methods that enable diagnostics and decision making in uncertain conditions.
Pattern recognition is a common feature in artificial intelligence and machine learning applications. In general it is used to assign a label or classification to a given input value by performing “most likely” matching of the inputs, taking into account their statistical variation. It is often confused with pattern matching which looks for exact matches in the data input with pre-existing pattern(s) such as is taught in U.S. Pat. No. 7,818,276 Veillette.
Pattern recognition is a technique typically devoid of a true understanding of the physical data. Relying solely on pattern recognition to classify a variation or a disturbance to a process for the basis of a control action is akin to taking a blind risk.
There is a need for apparatus and methods for controlling dynamic processes such that sufficient confidence and certainty in the process state exists so that automated control action can be taken with acceptable levels of risk.